Factor Analysis
Hannah Andrews
4/30/2021
Sample: There are 6157 complete cases on CAM variables at Wave 1.
CAM Descriptives
Frequencies and percentages on each variable
## Item Category Frequency Percent Pcnt_of_nonMissing
## 1 aAcupuncture 0 6083 98.8 98.8
## 2 aAcupuncture 1 74 1.2 1.2
## 3 aBiofeedback 0 6109 99.2 99.2
## 4 aBiofeedback 1 48 0.8 0.8
## 5 aChiropractic 0 5418 88.0 88.0
## 6 aChiropractic 1 739 12.0 12.0
## 7 aEnergyHeal 0 6064 98.5 98.5
## 8 aEnergyHeal 1 93 1.5 1.5
## 9 aExerciseMove 0 5079 82.5 82.5
## 10 aExerciseMove 1 1078 17.5 17.5
## 11 aHerbal 0 5856 95.1 95.1
## 12 aHerbal 1 301 4.9 4.9
## 13 aVitamins 0 5875 95.4 95.4
## 14 aVitamins 1 282 4.6 4.6
## 15 aHomeopathy 0 6015 97.7 97.7
## 16 aHomeopathy 1 142 2.3 2.3
## 17 aHypnosis 0 6085 98.8 98.8
## 18 aHypnosis 1 72 1.2 1.2
## 19 aImageryTech 0 5973 97.0 97.0
## 20 aImageryTech 1 184 3.0 3.0
## 21 aMassage 0 5638 91.6 91.6
## 22 aMassage 1 519 8.4 8.4
## 23 aPrayer 0 4315 70.1 70.1
## 24 aPrayer 1 1842 29.9 29.9
## 25 aRelaxMeditate 0 5344 86.8 86.8
## 26 aRelaxMeditate 1 813 13.2 13.2
## 27 aSpecialDiet 0 5488 89.1 89.1
## 28 aSpecialDiet 1 669 10.9 10.9
## 29 aSpiritHeal 0 5961 96.8 96.8
## 30 aSpiritHeal 1 196 3.2 3.2Visualization of frequencies
## Number of CAM treatments used in the past 12 months
## totalCams
## 0 1 2 3 4 5
## 0.4866006172 0.2320935521 0.1279844080 0.0729251259 0.0328081858 0.0190027611
## 6 7 8 9 10 11
## 0.0120188403 0.0063342537 0.0040604190 0.0021114179 0.0019490011 0.0014617509
## 13 15
## 0.0004872503 0.0001624168## Category f rf rf(%) cf cf(%)
## 0 2996 0.49 48.66 2996 48.66
## 1 1429 0.23 23.21 4425 71.87
## 2 788 0.13 12.80 5213 84.67
## 3 449 0.07 7.29 5662 91.96
## 4 202 0.03 3.28 5864 95.24
## 5 117 0.02 1.90 5981 97.14
## 6 74 0.01 1.20 6055 98.34
## 7 39 0.01 0.63 6094 98.98
## 8 25 0.00 0.41 6119 99.38
## 9 13 0.00 0.21 6132 99.59
## 10 12 0.00 0.19 6144 99.79
## 11 9 0.00 0.15 6153 99.94
## 13 3 0.00 0.05 6156 99.98
## 15 1 0.00 0.02 6157 100.00Analayses including all 15 CAMs
Correlation matrix
Create and plot the tetrachoric correlation matrix for all CAMs. 
Check that items are not perfectly correlated with each other.
## aA aB aC aEH aEM aHr aV aHm aHy aI aM aP aR aSD aSH
## aAcupuncture 1
## aBiofeedback . 1
## aChiropractic . 1
## aEnergyHeal . . . 1
## aExerciseMove . . 1
## aHerbal , . . , . 1
## aVitamins . . . , 1
## aHomeopathy . . . , . , , 1
## aHypnosis . . . . 1
## aImageryTech . . , . . . . . 1
## aMassage . . . , . . . . . . 1
## aPrayer . . . . . . 1
## aRelaxMeditate . , , . . . . . + . . 1
## aSpecialDiet . . . . . . . . . . 1
## aSpiritHeal . . . . . . , . . 1
## attr(,"legend")
## [1] 0 ' ' 0.3 '.' 0.6 ',' 0.8 '+' 0.9 '*' 0.95 'B' 1Test for correlation adequacy
I will test for correlation adequacy using Bartlett’s Sphericity test. This test tests the hypothesis that correlations between variables are greater than would be expected by chance. The null hypothesis states that all off diagonal are 0. If the null hypothesis is rejected there is correlation adequacy.
## $chisq
## [1] 66329.56
##
## $p.value
## [1] 0
##
## $df
## [1] 105I reject the null hypothesis. The CAM items are adequately correlated.
Test for sampling adequacy
I will test for sampling adequacy using the Kaiser-Meyer-Olkin (KMO) test.MSA refers to the overall measure of sampling adequacy. MSAi refer to the measure of sampling adequacy for each item. MSA is a measure of the proportion of variance among variables that might be common variance. The lower the proportion of variance that is common the more suited the data are for factor analysis.
MSA cutoffs: >.9 marvelous, .8s meritorious, .7s middling, .6s mediocre, .5s miserable, less than .5 is unacceptable.
## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = het.mat)
## Overall MSA = 0.76
## MSA for each item =
## aAcupuncture aBiofeedback aChiropractic aEnergyHeal aExerciseMove
## 0.85 0.75 0.73 0.89 0.77
## aHerbal aVitamins aHomeopathy aHypnosis aImageryTech
## 0.91 0.81 0.88 0.72 0.82
## aMassage aPrayer aRelaxMeditate aSpecialDiet aSpiritHeal
## 0.81 0.53 0.64 0.88 0.51Items that may be a concern with regard to sampling adequacy: prayer or other spiritual practices, relaxation or meditation, and spiritual healing. Overall MSA indicates sampling adequacy.
Determining the number of factors
First, I will run a parallel analysis. From RDocumentation: “``Parallel” analyis is a technique that compares the scree of factors of the observed data with that of a random data matrix of the same size as the original."
Parallel analysis completed using maximum likelihood factoring method. 
## Parallel analysis suggests that the number of factors = 6 and the number of components = NAI received many warning messages stating “A cell entry of 0 was replaced with correct = 0.5. Check your data!” This has to do with continuity when computing a tetrachoric correlation matrix. I added 1 to all values in the dataframe to check that the issue was not the 0/1 values. The results were the same.
From Statistics of DOOM notes: i. The dark line is set at one, which is part of the Kaiser criterion. This method is an older rule of thumb that is not well supported anymore. You would look at the number of eigenvalues that are greater than 1 (or .70 in new literature). This rule tends to overestimate the number of factors/components needed. ii. The red dotted line is the random data set used to test this analysis. Your data is randomly reordered to see how many factors are better than chance. iii. The blue line and triangles are your eigenvalues from the real dataset. iv. You want to look at where the blue and red lines cross.
The parallel analysis suggests 6 factors. This is where the lines cross. Looking at the scree plot, none of the drop offs appear to be very large. Seems like there are maybe 2 factors.
Note: Scree plots are a visual depiction of the eigenvalues. Look for the large drop off to figure out how many factors to use.
Kaiser Criterion
# older kaiser criterion, number of eigenvalues greater than 1
sum(nofactors$fa.values > 1.0)## [1] 1# new kaiser criterion, number of eigenvalues greater than 0.7
sum(nofactors$fa.values > .7)## [1] 2New kaiser criterion rule (eigenvalues greater than 0.7) suggests 2 factors.
After readings Ayers and Kronenfeld (2010), I will test the old CAM domains posted by the National Center for Complementary and Alternative Medicine (NCCAM), now the National Center for Complementary and Integrative Medicine (NCCIM). Then I will test the domains found by Ayers and Kronenfeld (2010). Finally, I will test the NCCIM’s current CAM domains before performing exploratory factor analysis on the MIDUS data.
Fit Statistics: All models
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.## Warning in fac(r = r, nfactors = nfactors, n.obs = n.obs, rotate = rotate, : An
## ultra-Heywood case was detected. Examine the results carefully## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.## Warning in fac(r = r, nfactors = nfactors, n.obs = n.obs, rotate = rotate, : An
## ultra-Heywood case was detected. Examine the results carefully## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.## Warning in fac(r = r, nfactors = nfactors, n.obs = n.obs, rotate = rotate, : An
## ultra-Heywood case was detected. Examine the results carefully## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.## Warning in fac(r = r, nfactors = nfactors, n.obs = n.obs, rotate = rotate, : An
## ultra-Heywood case was detected. Examine the results carefully## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.## Warning in fac(r = r, nfactors = nfactors, n.obs = n.obs, rotate = rotate, : An
## ultra-Heywood case was detected. Examine the results carefully## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.## Warning in fac(r = r, nfactors = nfactors, n.obs = n.obs, rotate = rotate, : An
## ultra-Heywood case was detected. Examine the results carefully## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.## Warning in fac(r = r, nfactors = nfactors, n.obs = n.obs, rotate = rotate, : An
## ultra-Heywood case was detected. Examine the results carefully## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.## Warning in fac(r = r, nfactors = nfactors, n.obs = n.obs, rotate = rotate, : An
## ultra-Heywood case was detected. Examine the results carefully## maximum iteration exceeded## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## An ultra-Heywood case was detected. Examine the results carefully## maximum iteration exceeded## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## An ultra-Heywood case was detected. Examine the results carefully## maximum iteration exceeded## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## An ultra-Heywood case was detected. Examine the results carefully## maximum iteration exceeded## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## An ultra-Heywood case was detected. Examine the results carefully## Factors FactorMethod Rotation BIC TLI CFI RMSR
## 1mlvarimax 1 ml varimax 22422.125 0.5927 0.6509 0.0918
## 1mlpromax 1 ml promax 22422.125 0.5927 0.6509 0.0918
## 1mloblimin 1 ml oblimin 22422.125 0.5927 0.6509 0.0918
## 1mlcluster 1 ml cluster 22422.125 0.5927 0.6509 0.0918
## 1minresvarimax 1 minres varimax 22461.997 0.5920 0.6503 0.0912
## 1minrespromax 1 minres promax 22461.997 0.5920 0.6503 0.0912
## 1minresoblimin 1 minres oblimin 22461.997 0.5920 0.6503 0.0912
## 1minrescluster 1 minres cluster 22461.997 0.5920 0.6503 0.0912
## 1pavarimax 1 pa varimax 22461.655 0.5920 0.6503 0.0912
## 1papromax 1 pa promax 22461.655 0.5920 0.6503 0.0912
## 1paoblimin 1 pa oblimin 22461.655 0.5920 0.6503 0.0912
## 1pacluster 1 pa cluster 22461.655 0.5920 0.6503 0.0912
## 2mlvarimax 2 ml varimax 17875.291 0.6148 0.7212 0.0748
## 2mlpromax 2 ml promax 17875.291 0.6148 0.7212 0.0748
## 2mloblimin 2 ml oblimin 17875.291 0.6148 0.7212 0.0748
## 2mlcluster 2 ml cluster 17875.291 0.6148 0.7212 0.0748
## 2minresvarimax 2 minres varimax 18414.251 0.6035 0.7131 0.0718
## 2minrespromax 2 minres promax 18414.251 0.6035 0.7131 0.0718
## 2minresoblimin 2 minres oblimin 18414.251 0.6035 0.7131 0.0718
## 2minrescluster 2 minres cluster 18414.251 0.6035 0.7131 0.0718
## 2pavarimax 2 pa varimax 18390.817 0.6040 0.7134 0.0718
## 2papromax 2 pa promax 18390.817 0.6040 0.7134 0.0718
## 2paoblimin 2 pa oblimin 18390.817 0.6040 0.7134 0.0718
## 2pacluster 2 pa cluster 18390.817 0.6040 0.7134 0.0718
## 3mlvarimax 3 ml varimax 11028.395 0.7101 0.8261 0.0632
## 3mlpromax 3 ml promax 11028.395 0.7101 0.8261 0.0632
## 3mloblimin 3 ml oblimin 11028.395 0.7101 0.8261 0.0632
## 3mlcluster 3 ml cluster 11028.395 0.7101 0.8261 0.0632
## 3minresvarimax 3 minres varimax 12896.066 0.6631 0.7979 0.0564
## 3minrespromax 3 minres promax 12896.066 0.6631 0.7979 0.0564
## 3minresoblimin 3 minres oblimin 12896.066 0.6631 0.7979 0.0564
## 3minrescluster 3 minres cluster 12896.066 0.6631 0.7979 0.0564
## 3pavarimax 3 pa varimax 12919.180 0.6625 0.7976 0.0564
## 3papromax 3 pa promax 12919.180 0.6625 0.7976 0.0564
## 3paoblimin 3 pa oblimin 12919.180 0.6625 0.7976 0.0564
## 3pacluster 3 pa cluster 12919.180 0.6625 0.7976 0.0564
## 4mlvarimax 4 ml varimax 7272.309 0.7616 0.8842 0.0556
## 4mlpromax 4 ml promax 7272.309 0.7616 0.8842 0.0556
## 4mloblimin 4 ml oblimin 7272.309 0.7616 0.8842 0.0556
## 4mlcluster 4 ml cluster 7272.309 0.7616 0.8842 0.0556
## 4minresvarimax 4 minres varimax 10755.623 0.6532 0.8316 0.0434
## 4minrespromax 4 minres promax 10755.623 0.6532 0.8316 0.0434
## 4minresoblimin 4 minres oblimin 10755.623 0.6532 0.8316 0.0434
## 4minrescluster 4 minres cluster 10755.623 0.6532 0.8316 0.0434
## 4pavarimax 4 pa varimax 10935.463 0.6476 0.8289 0.0440
## 4papromax 4 pa promax 10935.463 0.6476 0.8289 0.0440
## 4paoblimin 4 pa oblimin 10935.463 0.6476 0.8289 0.0440
## 4pacluster 4 pa cluster 10935.463 0.6476 0.8289 0.0440
## 5mlvarimax 5 ml varimax 4600.918 0.8053 0.9259 0.0419
## 5mlpromax 5 ml promax 4600.918 0.8053 0.9259 0.0419
## 5mloblimin 5 ml oblimin 4600.918 0.8053 0.9259 0.0419
## 5mlcluster 5 ml cluster 4600.918 0.8053 0.9259 0.0419
## 5minresvarimax 5 minres varimax 7198.159 0.7023 0.8866 0.0286
## 5minrespromax 5 minres promax 7198.159 0.7023 0.8866 0.0286
## 5minresoblimin 5 minres oblimin 7198.159 0.7023 0.8866 0.0286
## 5minrescluster 5 minres cluster 7198.159 0.7023 0.8866 0.0286
## 5pavarimax 5 pa varimax 7912.753 0.6739 0.8759 0.0309
## 5papromax 5 pa promax 7912.753 0.6739 0.8759 0.0309
## 5paoblimin 5 pa oblimin 7912.753 0.6739 0.8759 0.0309
## 5pacluster 5 pa cluster 7912.753 0.6739 0.8759 0.0309
## 6mlvarimax 6 ml varimax 3151.229 0.8211 0.9489 0.0290
## 6mlpromax 6 ml promax 3151.229 0.8211 0.9489 0.0290
## 6mloblimin 6 ml oblimin 3151.229 0.8211 0.9489 0.0290
## 6mlcluster 6 ml cluster 3151.229 0.8211 0.9489 0.0290
## 6minresvarimax 6 minres varimax 6429.609 0.6477 0.8994 0.0224
## 6minrespromax 6 minres promax 6429.609 0.6477 0.8994 0.0224
## 6minresoblimin 6 minres oblimin 6429.609 0.6477 0.8994 0.0224
## 6minrescluster 6 minres cluster 6429.609 0.6477 0.8994 0.0224
## 6pavarimax 6 pa varimax 5001.737 0.7232 0.9210 0.0214
## 6papromax 6 pa promax 5001.737 0.7232 0.9210 0.0214
## 6paoblimin 6 pa oblimin 5001.737 0.7232 0.9210 0.0214
## 6pacluster 6 pa cluster 5001.737 0.7232 0.9210 0.0214
## RMSEA
## 1mlvarimax 0.2043
## 1mlpromax 0.2043
## 1mloblimin 0.2043
## 1mlcluster 0.2043
## 1minresvarimax 0.2044
## 1minrespromax 0.2044
## 1minresoblimin 0.2044
## 1minrescluster 0.2044
## 1pavarimax 0.2044
## 1papromax 0.2044
## 1paoblimin 0.2044
## 1pacluster 0.2044
## 2mlvarimax 0.1986
## 2mlpromax 0.1986
## 2mloblimin 0.1986
## 2mlcluster 0.1986
## 2minresvarimax 0.2015
## 2minrespromax 0.2015
## 2minresoblimin 0.2015
## 2minrescluster 0.2015
## 2pavarimax 0.2014
## 2papromax 0.2014
## 2paoblimin 0.2014
## 2pacluster 0.2014
## 3mlvarimax 0.1723
## 3mlpromax 0.1723
## 3mloblimin 0.1723
## 3mlcluster 0.1723
## 3minresvarimax 0.1857
## 3minrespromax 0.1857
## 3minresoblimin 0.1857
## 3minrescluster 0.1857
## 3pavarimax 0.1859
## 3papromax 0.1859
## 3paoblimin 0.1859
## 3pacluster 0.1859
## 4mlvarimax 0.1563
## 4mlpromax 0.1563
## 4mloblimin 0.1563
## 4mlcluster 0.1563
## 4minresvarimax 0.1884
## 4minrespromax 0.1884
## 4minresoblimin 0.1884
## 4minrescluster 0.1884
## 4pavarimax 0.1899
## 4papromax 0.1899
## 4paoblimin 0.1899
## 4pacluster 0.1899
## 5mlvarimax 0.1412
## 5mlpromax 0.1412
## 5mloblimin 0.1412
## 5mlcluster 0.1412
## 5minresvarimax 0.1746
## 5minrespromax 0.1746
## 5minresoblimin 0.1746
## 5minrescluster 0.1746
## 5pavarimax 0.1827
## 5papromax 0.1827
## 5paoblimin 0.1827
## 5pacluster 0.1827
## 6mlvarimax 0.1353
## 6mlpromax 0.1353
## 6mloblimin 0.1353
## 6mlcluster 0.1353
## 6minresvarimax 0.1899
## 6minrespromax 0.1899
## 6minresoblimin 0.1899
## 6minrescluster 0.1899
## 6pavarimax 0.1683
## 6papromax 0.1683
## 6paoblimin 0.1683
## 6pacluster 0.1683Factor loadings: All Models
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.## Warning in fac(r = r, nfactors = nfactors, n.obs = n.obs, rotate = rotate, : An
## ultra-Heywood case was detected. Examine the results carefully
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## An ultra-Heywood case was detected. Examine the results carefully
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## An ultra-Heywood case was detected. Examine the results carefully
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## An ultra-Heywood case was detected. Examine the results carefully
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## An ultra-Heywood case was detected. Examine the results carefully
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## An ultra-Heywood case was detected. Examine the results carefully
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## An ultra-Heywood case was detected. Examine the results carefully
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## An ultra-Heywood case was detected. Examine the results carefully
## maximum iteration exceeded## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## An ultra-Heywood case was detected. Examine the results carefully
## maximum iteration exceeded## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## An ultra-Heywood case was detected. Examine the results carefully
## maximum iteration exceeded## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## An ultra-Heywood case was detected. Examine the results carefully
## maximum iteration exceeded## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## An ultra-Heywood case was detected. Examine the results carefully
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## NULLExclude prayer and spiritual healing
Tetrachoric correlations (excluding prayer and spiritual healing)
Test for correlation adequacy
Bartlett’s test for sphericity.
## $chisq
## [1] 51305.93
##
## $p.value
## [1] 0
##
## $df
## [1] 78Items are adequately correlated.
Test for sampling adequacy
MSA cutoffs: >.9 marvelous, .8s meritorious, .7s middling, .6s mediocre, .5s miserable, less than .5 is unacceptable.
## [1] 0.886576## aChiropractic aHypnosis aBiofeedback aExerciseMove aRelaxMeditate
## 0.8014794 0.8513482 0.8561802 0.8593921 0.8623881
## aMassage aImageryTech aSpecialDiet aHerbal aAcupuncture
## 0.8803881 0.8905944 0.8923741 0.8965394 0.8988504
## aHomeopathy aVitamins aEnergyHeal
## 0.9057442 0.9083067 0.9361243Determining number of factors
Parallel Analysis
## Parallel analysis suggests that the number of factors = 5 and the number of components = NAThe parallel analysis suggests 5 factors. There is a drop off on the scree plot after 4.
Kaiser Criterion
Older kaiser criterion suggests number of factors is equal to the number of eigenvalues greater than 1. The new kaiser criterion suggests numbers of factors is equal to the number of eigenvalues greater than 0.7
## [1] 6.5668 1.1133 1.0560 1.0129 0.7291 0.5862 0.4995 0.3394 0.2891 0.2793
## [11] 0.2126 0.1776 0.1380Suggests 4 or 5 factors.
Reliability among ACAMs excluding prayer and spiritual healing
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd
## 0.6826334 0.712388 0.713637 0.1600388 2.476906 0.005549916 0.0626429 0.107813
## median_r
## 0.1581703Removing prayer and spiritual healing does not improve the reliability at all. Raw alpha is almost exactly the same, 0.68. Lowers reliability slightly (from 0.695).
##Fit Statistics: All models
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.## Factors FactorMethod Rotation BIC TLI CFI RMSR
## 2mlvarimax 2 ml varimax 6071.832 0.8138 0.8735 0.0648
## 2mlpromax 2 ml promax 6071.832 0.8138 0.8735 0.0648
## 2mloblimin 2 ml oblimin 6071.832 0.8138 0.8735 0.0648
## 2mlcluster 2 ml cluster 6071.832 0.8138 0.8735 0.0648
## 2minresvarimax 2 minres varimax 6259.199 0.8084 0.8698 0.0628
## 2minrespromax 2 minres promax 6259.199 0.8084 0.8698 0.0628
## 2minresoblimin 2 minres oblimin 6259.199 0.8084 0.8698 0.0628
## 2minrescluster 2 minres cluster 6259.199 0.8084 0.8698 0.0628
## 3mlvarimax 3 ml varimax 4139.058 0.8381 0.9129 0.0510
## 3mlpromax 3 ml promax 4139.058 0.8381 0.9129 0.0510
## 3mloblimin 3 ml oblimin 4139.058 0.8381 0.9129 0.0510
## 3mlcluster 3 ml cluster 4139.058 0.8381 0.9129 0.0510
## 3minresvarimax 3 minres varimax 4206.168 0.8357 0.9116 0.0479
## 3minrespromax 3 minres promax 4206.168 0.8357 0.9116 0.0479
## 3minresoblimin 3 minres oblimin 4206.168 0.8357 0.9116 0.0479
## 3minrescluster 3 minres cluster 4206.168 0.8357 0.9116 0.0479
## 4mlvarimax 4 ml varimax 2266.928 0.8803 0.9509 0.0300
## 4mlpromax 4 ml promax 2266.928 0.8803 0.9509 0.0300
## 4mloblimin 4 ml oblimin 2266.928 0.8803 0.9509 0.0300
## 4mlcluster 4 ml cluster 2266.928 0.8803 0.9509 0.0300
## 4minresvarimax 4 minres varimax 2436.929 0.8722 0.9476 0.0283
## 4minrespromax 4 minres promax 2436.929 0.8722 0.9476 0.0283
## 4minresoblimin 4 minres oblimin 2436.929 0.8722 0.9476 0.0283
## 4minrescluster 4 minres cluster 2436.929 0.8722 0.9476 0.0283
## 5mlvarimax 5 ml varimax 1167.187 0.9109 0.9737 0.0201
## 5mlpromax 5 ml promax 1167.187 0.9109 0.9737 0.0201
## 5mloblimin 5 ml oblimin 1167.187 0.9109 0.9737 0.0201
## 5mlcluster 5 ml cluster 1167.187 0.9109 0.9737 0.0201
## 5minresvarimax 5 minres varimax 1352.568 0.8986 0.9701 0.0183
## 5minrespromax 5 minres promax 1352.568 0.8986 0.9701 0.0183
## 5minresoblimin 5 minres oblimin 1352.568 0.8986 0.9701 0.0183
## 5minrescluster 5 minres cluster 1352.568 0.8986 0.9701 0.0183
## RMSEA
## 2mlvarimax 0.1409
## 2mlpromax 0.1409
## 2mloblimin 0.1409
## 2mlcluster 0.1409
## 2minresvarimax 0.1430
## 2minrespromax 0.1430
## 2minresoblimin 0.1430
## 2minrescluster 0.1430
## 3mlvarimax 0.1314
## 3mlpromax 0.1314
## 3mloblimin 0.1314
## 3mlcluster 0.1314
## 3minresvarimax 0.1324
## 3minrespromax 0.1324
## 3minresoblimin 0.1324
## 3minrescluster 0.1324
## 4mlvarimax 0.1130
## 4mlpromax 0.1130
## 4mloblimin 0.1130
## 4mlcluster 0.1130
## 4minresvarimax 0.1167
## 4minrespromax 0.1167
## 4minresoblimin 0.1167
## 4minrescluster 0.1167
## 5mlvarimax 0.0975
## 5mlpromax 0.0975
## 5mloblimin 0.0975
## 5mlcluster 0.0975
## 5minresvarimax 0.1040
## 5minrespromax 0.1040
## 5minresoblimin 0.1040
## 5minrescluster 0.1040Factor loadings: 3 through 5 factor models
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.
## [[1]]
## [[1]][[1]]
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## NULLExclude prayer, spiritual healing, and energy healing
Tetrachoric correlation matrix
## Bartlett’s Test of Spherecity I will test for correlation adequacy using Bartlett’s Sphericity test. This test tests the hypothesis that correlations between variables are greater than would be expected by chance. The null hypothesis states that all off diagonal are 0. If the null hypothesis is rejected there is correlation adequacy.
## $chisq
## [1] 42574.65
##
## $p.value
## [1] 0
##
## $df
## [1] 66KMO Test for sampling adequacy
MSA cutoffs: >.9 marvelous, .8s meritorious, .7s middling, .6s mediocre, .5s miserable, less than .5 is unacceptable.
## [1] 0.8625341## aChiropractic aRelaxMeditate aExerciseMove aBiofeedback aHypnosis
## 0.7969845 0.8321353 0.8337881 0.8487751 0.8574957
## aMassage aImageryTech aHerbal aAcupuncture aSpecialDiet
## 0.8660164 0.8707506 0.8708735 0.8737196 0.8834541
## aHomeopathy aVitamins
## 0.8912350 0.8944847Great.
Parallel Analysis
First, I will run a parallel analysis. From RDocumentation: “``Parallel” analyis is a technique that compares the scree of factors of the observed data with that of a random data matrix of the same size as the original."
## Parallel analysis suggests that the number of factors = 5 and the number of components = NAThe parallel analysis suggests 5 factors. This is where the lines cross. Looking at the scree plot, there is a drop at 4 factors.
Kaiser Criterion
Older kaiser criterion suggests number of factors is equal to the number of eigenvalues greater than 1. The new kaiser criterion suggests numbers of factors is equal to the number of eigenvalues greater than 0.7
## [1] 5.8080 1.1060 1.0447 1.0128 0.7267 0.5841 0.4935 0.3361 0.2866 0.2784
## [11] 0.1776 0.1455Indicates 4 or 5 factors.
##Fit Statistics: All models
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.## Warning in fac(r = r, nfactors = nfactors, n.obs = n.obs, rotate = rotate, : An
## ultra-Heywood case was detected. Examine the results carefully## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.## Warning in fac(r = r, nfactors = nfactors, n.obs = n.obs, rotate = rotate, : An
## ultra-Heywood case was detected. Examine the results carefully## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.## Warning in fac(r = r, nfactors = nfactors, n.obs = n.obs, rotate = rotate, : An
## ultra-Heywood case was detected. Examine the results carefully## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.## Warning in fac(r = r, nfactors = nfactors, n.obs = n.obs, rotate = rotate, : An
## ultra-Heywood case was detected. Examine the results carefully## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.## Warning in fac(r = r, nfactors = nfactors, n.obs = n.obs, rotate = rotate, : An
## ultra-Heywood case was detected. Examine the results carefully## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.## Warning in fac(r = r, nfactors = nfactors, n.obs = n.obs, rotate = rotate, : An
## ultra-Heywood case was detected. Examine the results carefully## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.## Warning in fac(r = r, nfactors = nfactors, n.obs = n.obs, rotate = rotate, : An
## ultra-Heywood case was detected. Examine the results carefully## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.## Warning in fac(r = r, nfactors = nfactors, n.obs = n.obs, rotate = rotate, : An
## ultra-Heywood case was detected. Examine the results carefully## Factors FactorMethod Rotation BIC TLI CFI RMSR
## 2mlvarimax 2 ml varimax 5469.563 0.7905 0.8635 0.0696
## 2mlpromax 2 ml promax 5469.563 0.7905 0.8635 0.0696
## 2mloblimin 2 ml oblimin 5469.563 0.7905 0.8635 0.0696
## 2mlcluster 2 ml cluster 5469.563 0.7905 0.8635 0.0696
## 2minresvarimax 2 minres varimax 5678.340 0.7829 0.8586 0.0669
## 2minrespromax 2 minres promax 5678.340 0.7829 0.8586 0.0669
## 2minresoblimin 2 minres oblimin 5678.340 0.7829 0.8586 0.0669
## 2minrescluster 2 minres cluster 5678.340 0.7829 0.8586 0.0669
## 2pavarimax 2 pa varimax 5669.949 0.7832 0.8588 0.0669
## 2papromax 2 pa promax 5669.949 0.7832 0.8588 0.0669
## 2paoblimin 2 pa oblimin 5669.949 0.7832 0.8588 0.0669
## 2pacluster 2 pa cluster 5669.949 0.7832 0.8588 0.0669
## 3mlvarimax 3 ml varimax 3395.890 0.8282 0.9141 0.0536
## 3mlpromax 3 ml promax 3395.890 0.8282 0.9141 0.0536
## 3mloblimin 3 ml oblimin 3395.890 0.8282 0.9141 0.0536
## 3mlcluster 3 ml cluster 3395.890 0.8282 0.9141 0.0536
## 3minresvarimax 3 minres varimax 3690.383 0.8143 0.9072 0.0507
## 3minrespromax 3 minres promax 3690.383 0.8143 0.9072 0.0507
## 3minresoblimin 3 minres oblimin 3690.383 0.8143 0.9072 0.0507
## 3minrescluster 3 minres cluster 3690.383 0.8143 0.9072 0.0507
## 3pavarimax 3 pa varimax 3674.177 0.8151 0.9076 0.0507
## 3papromax 3 pa promax 3674.177 0.8151 0.9076 0.0507
## 3paoblimin 3 pa oblimin 3674.177 0.8151 0.9076 0.0507
## 3pacluster 3 pa cluster 3674.177 0.8151 0.9076 0.0507
## 4mlvarimax 4 ml varimax 1788.721 0.8722 0.9536 0.0317
## 4mlpromax 4 ml promax 1788.721 0.8722 0.9536 0.0317
## 4mloblimin 4 ml oblimin 1788.721 0.8722 0.9536 0.0317
## 4mlcluster 4 ml cluster 1788.721 0.8722 0.9536 0.0317
## 4minresvarimax 4 minres varimax 2012.150 0.8578 0.9483 0.0287
## 4minrespromax 4 minres promax 2012.150 0.8578 0.9483 0.0287
## 4minresoblimin 4 minres oblimin 2012.150 0.8578 0.9483 0.0287
## 4minrescluster 4 minres cluster 2012.150 0.8578 0.9483 0.0287
## 4pavarimax 4 pa varimax 2008.638 0.8580 0.9484 0.0287
## 4papromax 4 pa promax 2008.638 0.8580 0.9484 0.0287
## 4paoblimin 4 pa oblimin 2008.638 0.8580 0.9484 0.0287
## 4pacluster 4 pa cluster 2008.638 0.8580 0.9484 0.0287
## 5mlvarimax 5 ml varimax 912.527 0.8994 0.9756 0.0205
## 5mlpromax 5 ml promax 912.527 0.8994 0.9756 0.0205
## 5mloblimin 5 ml oblimin 912.527 0.8994 0.9756 0.0205
## 5mlcluster 5 ml cluster 912.527 0.8994 0.9756 0.0205
## 5minresvarimax 5 minres varimax 1060.848 0.8850 0.9721 0.0184
## 5minrespromax 5 minres promax 1060.848 0.8850 0.9721 0.0184
## 5minresoblimin 5 minres oblimin 1060.848 0.8850 0.9721 0.0184
## 5minrescluster 5 minres cluster 1060.848 0.8850 0.9721 0.0184
## 5pavarimax 5 pa varimax 1082.307 0.8829 0.9716 0.0184
## 5papromax 5 pa promax 1082.307 0.8829 0.9716 0.0184
## 5paoblimin 5 pa oblimin 1082.307 0.8829 0.9716 0.0184
## 5pacluster 5 pa cluster 1082.307 0.8829 0.9716 0.0184
## RMSEA
## 2mlvarimax 0.1480
## 2mlpromax 0.1480
## 2mloblimin 0.1480
## 2mlcluster 0.1480
## 2minresvarimax 0.1507
## 2minrespromax 0.1507
## 2minresoblimin 0.1507
## 2minrescluster 0.1507
## 2pavarimax 0.1506
## 2papromax 0.1506
## 2paoblimin 0.1506
## 2pacluster 0.1506
## 3mlvarimax 0.1340
## 3mlpromax 0.1340
## 3mloblimin 0.1340
## 3mlcluster 0.1340
## 3minresvarimax 0.1393
## 3minrespromax 0.1393
## 3minresoblimin 0.1393
## 3minrescluster 0.1393
## 3pavarimax 0.1391
## 3papromax 0.1391
## 3paoblimin 0.1391
## 3pacluster 0.1391
## 4mlvarimax 0.1156
## 4mlpromax 0.1156
## 4mloblimin 0.1156
## 4mlcluster 0.1156
## 4minresvarimax 0.1219
## 4minrespromax 0.1219
## 4minresoblimin 0.1219
## 4minrescluster 0.1219
## 4pavarimax 0.1219
## 4papromax 0.1219
## 4paoblimin 0.1219
## 4pacluster 0.1219
## 5mlvarimax 0.1026
## 5mlpromax 0.1026
## 5mloblimin 0.1026
## 5mlcluster 0.1026
## 5minresvarimax 0.1097
## 5minrespromax 0.1097
## 5minresoblimin 0.1097
## 5minrescluster 0.1097
## 5pavarimax 0.1106
## 5papromax 0.1106
## 5paoblimin 0.1106
## 5pacluster 0.1106Factor loadings: Three through 5 factor models
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.## Warning in fac(r = r, nfactors = nfactors, n.obs = n.obs, rotate = rotate, : An
## ultra-Heywood case was detected. Examine the results carefully
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## An ultra-Heywood case was detected. Examine the results carefully
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## An ultra-Heywood case was detected. Examine the results carefully
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## An ultra-Heywood case was detected. Examine the results carefully
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## An ultra-Heywood case was detected. Examine the results carefully
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## An ultra-Heywood case was detected. Examine the results carefully
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## An ultra-Heywood case was detected. Examine the results carefully
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## An ultra-Heywood case was detected. Examine the results carefully
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## NULLExcluding prayer, spiritual healing, biofeedback, and hypnosis.
Tetrachoric correlations
## Bartlett’s Test of Spherecity I will test for correlation adequacy using Bartlett’s Sphericity test. This test tests the hypothesis that correlations between variables are greater than would be expected by chance. The null hypothesis states that all off diagonal are 0. If the null hypothesis is rejected there is correlation adequacy.
## $chisq
## [1] 8540.367
##
## $p.value
## [1] 0
##
## $df
## [1] 55KMO Test for sampling adequacy
MSA cutoffs: >.9 marvelous, .8s meritorious, .7s middling, .6s mediocre, .5s miserable, less than .5 is unacceptable.
## [1] 0.8119214## aChiropractic aImageryTech aMassage aRelaxMeditate aExerciseMove
## 0.7597641 0.7805104 0.7990479 0.8100055 0.8103004
## aHerbal aHomeopathy aSpecialDiet aVitamins aEnergyHeal
## 0.8123552 0.8125993 0.8307527 0.8385329 0.8390753
## aAcupuncture
## 0.8410255Parallel Analysis
## Parallel analysis suggests that the number of factors = 4 and the number of components = NASuggests 4 factors.
Kaiser Criterion
## [1] 2.9624 1.1136 1.0788 1.0116 0.8698 0.7710 0.7337 0.6914 0.6340 0.5710
## [11] 0.5627Suggests 4 factors. ## Fit Statistics: All models
## Factors FactorMethod Rotation BIC TLI CFI RMSR
## 3mlvarimax 3 ml varimax 108.0315 0.9219 0.9645 0.0253
## 3mlpromax 3 ml promax 108.0315 0.9219 0.9645 0.0253
## 3mloblimin 3 ml oblimin 108.0315 0.9219 0.9645 0.0253
## 3mlcluster 3 ml cluster 108.0315 0.9219 0.9645 0.0253
## 3minresvarimax 3 minres varimax 112.3103 0.9208 0.9640 0.0252
## 3minrespromax 3 minres promax 112.3103 0.9208 0.9640 0.0252
## 3minresoblimin 3 minres oblimin 112.3103 0.9208 0.9640 0.0252
## 3minrescluster 3 minres cluster 112.3103 0.9208 0.9640 0.0252
## 4mlvarimax 4 ml varimax -89.2553 0.9840 0.9950 0.0100
## 4mlpromax 4 ml promax -89.2553 0.9840 0.9950 0.0100
## 4mloblimin 4 ml oblimin -89.2553 0.9840 0.9950 0.0100
## 4mlcluster 4 ml cluster -89.2553 0.9840 0.9950 0.0100
## 4minresvarimax 4 minres varimax -88.0262 0.9835 0.9949 0.0098
## 4minrespromax 4 minres promax -88.0262 0.9835 0.9949 0.0098
## 4minresoblimin 4 minres oblimin -88.0262 0.9835 0.9949 0.0098
## 4minrescluster 4 minres cluster -88.0262 0.9835 0.9949 0.0098
## 5mlvarimax 5 ml varimax -60.6944 0.9893 0.9980 0.0063
## 5mlpromax 5 ml promax -60.6944 0.9893 0.9980 0.0063
## 5mloblimin 5 ml oblimin -60.6944 0.9893 0.9980 0.0063
## 5mlcluster 5 ml cluster -60.6944 0.9893 0.9980 0.0063
## 5minresvarimax 5 minres varimax -60.8845 0.9894 0.9981 0.0060
## 5minrespromax 5 minres promax -60.8845 0.9894 0.9981 0.0060
## 5minresoblimin 5 minres oblimin -60.8845 0.9894 0.9981 0.0060
## 5minrescluster 5 minres cluster -60.8845 0.9894 0.9981 0.0060
## RMSEA
## 3mlvarimax 0.0442
## 3mlpromax 0.0442
## 3mloblimin 0.0442
## 3mlcluster 0.0442
## 3minresvarimax 0.0445
## 3minrespromax 0.0445
## 3minresoblimin 0.0445
## 3minrescluster 0.0445
## 4mlvarimax 0.0200
## 4mlpromax 0.0200
## 4mloblimin 0.0200
## 4mlcluster 0.0200
## 4minresvarimax 0.0203
## 4minrespromax 0.0203
## 4minresoblimin 0.0203
## 4minrescluster 0.0203
## 5mlvarimax 0.0164
## 5mlpromax 0.0164
## 5mloblimin 0.0164
## 5mlcluster 0.0164
## 5minresvarimax 0.0163
## 5minrespromax 0.0163
## 5minresoblimin 0.0163
## 5minrescluster 0.0163Factor loadings: 3 through 5 factor models
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